Thesis Details
Re-identifikace vozidla pomocí rozpoznání jeho registrační značky
This thesis aims at proposing vehicle license plate detection and recognition algorithms, suitable for vehicle re-identification. Simple urban traffic analysis system is also proposed. Multiple stages of this system was developed and tested. Specifically - vehicle detection, license plate detection and recognition. Vehicle detection is based on background substraction method, which results in an average hit rate of ~92%. License plate detection is done by cascade classifiers and achieves an average hit rate of 81.92% and precision rate of 94.42%. License plate recognition based on Template matching results in an average precission rate of 60.55%. Therefore the new license plate recognition method based on license plate scanning using the sliding window principle and neural network recognition was introduced. Neural network achieves a precision rate of 64.47% for five input features. Low precision rate of neural network is caused by small amount of training sample for some specific license plate characters.
Computer vision, Urban traffic, Urban traffic analysis, Vehicle detection, License plate detection, License plate recognition, Re-identification of vehicles, Background substraction, Cascade classifier, Template matching, Sliding window, License plate scanning, Neural network
Herout Adam, prof. Ing., Ph.D. (DCGM FIT BUT), člen
Rogalewicz Adam, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Sedlák Petr, doc. Ing., Ph.D. (DPHYS FEEC BUT), člen
Szőke Igor, Ing., Ph.D. (DCGM FIT BUT), člen
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY FIT BUT), člen
@mastersthesis{FITMT17858,
author = "Jakub \v{S}pa\v{n}hel",
type = "Master's thesis",
title = "Re-identifikace vozidla pomoc\'{i} rozpozn\'{a}n\'{i} jeho registra\v{c}n\'{i} zna\v{c}ky",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2015,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/17858/"
}